The analysis of tracking data in tactical game analysis is a topic of rising interest, as more detailed insights into performance structure in soccer can be obtained compared to traditional (e.g. notational) analyzes. Compared to the variety and detailed analyzes of offensive play, the number of studies analyzing the defensive play is low. However, in recent years, an increasing number of studies investigating defensive play have been published, so it seems useful to provide an overview of the current state of research in this area. Therefore, this study aims to identify the approaches that have been used to analyze the defensive play in professional soccer using player tracking data and to reveal the findings on successful defensive play. A systematic literature search of electronic databases (PubMed ( n = 604), Web of Science ( n = 593), and SPORTDiscuss ( n = 872)) was conducted according to the PRISMA extension for Scoping Reviews (PRISMA-ScR). Studies that were included used tracking data of professional adult male soccer and analyzed defensive play. The result is a total of 23 studies that were analyzed in detail using the standardized quality assessment checklist for systematic reviews in sports science. The synthesis of results was carried out descriptively by organizing the results into different levels of tactical play (individual level, group level, team level). All included studies were of good methodological quality. The approaches to investigate defensive play using tracking data are highly heterogeneous (e.g. analysis of defensive pressure, analysis of synchronization, behavioral analyzes, ball recoveries). Successful defensive play is characterized by high pressure at the individual level, by high inter-team and intra-team synchronization and balanced defense at the group level, and by a compact coordinated organization at the team level. By summarizing the state of research on defensive play in soccer using sophisticated analysis approaches that showcase the possibilities of tracking data, this study provides an important foundation for future research in this area.
The aim of the study was to examine the impact of the positional role and the individuality on the technical match performance in professional soccer players. From official match data of the Bundesliga season 2018/19, technical performance [short (<10 m)/medium (10–30 m)/long (>30 m) passes, dribblings, ball possessions] of all players who played during the season were analyzed (normative data). Five playing positions (center back, full back, central midfielder, wide midfielder and forward) were distinguished. As the contextual factor tactical formation is known to influence match performance, this parameter was controlled for. Further, those players who played at minimum four games in at least two different playing positions were included in the study sample (n = 13). The technical match performance of the players was analyzed in relation to the normative data regarding the extent to which the players either adapted or maintained their performance when changing the playing position. When switching playing positions, positional role could explain 3–6% of the variance in short passes and ball possessions and 27–44% of the variance in dribblings, medium passes, and long passes. Moreover, we observed large interindividual differences in the extent to which a player changed, adapted, or maintained his performance. In detail, five players clearly adapted their technical performance when changing playing positions, while five players maintained their performance. Coaches can use these findings to better understand the technical match performance of single players and further, to estimate the impact of a change in the positional role on the technical performance of the respective player.
With the growing availability of position data in sports, spatiotemporal analysis in soccer is a topic of rising interest. The aim of this study is to validate a performance indicator, namely D-Def, measuring passing effectiveness. D-Def calculates the change of the teams’ centroid, centroids of formation lines (e.g., defensive line), teams’ surface area, and teams’ spread in the following three seconds after a pass and therefore results in a measure of disruption of the opponents’ defense following a pass. While this measure was introduced earlier, in this study we aim to prove the usefulness to evaluate attacking sequences. In this study, 258 games of Dutch Eredivisie season 2018/19 were included, resulting in 13,094 attacks. D-Def, pass length, pass velocity, and pass angle of the last four passes of each attack were calculated and compared between successful and unsuccessful attacks. D-Def showed higher values for passes of successful compared to unsuccessful attacks (0.001 < p ≤ 0.029, 0.06 ≤ d ≤ 0.23). This difference showed the highest effects sizes in the penultimate pass (d = 0.23) and the maximal D-Def value of an attack (d = 0.23). Passing length (0.001 < p ≤ 0.236, 0.08 ≤ d ≤ 0.17) and passing velocity (0.001 < p ≤ 0.690, −0.09 ≤ d ≤ 0.12) showed inconsistent results in discriminating between successful and unsuccessful attacks. The results indicate that D-Def is a useful indicator for the measurement of pass effectiveness in attacking sequences, highlighting that successful attacks are connected to disruptive passing. Within successful attacks, at least one high disruptive action (pass with D-Def > 28) needs to be present. In addition, the penultimate pass (“hockey assist”) of an attack seems crucial in characterizing successful attacks.
The tactical formation has been shown to influence the match performance of professional soccer players. This study aimed to examine the effects of in-game changes in tactical formation on match performance and to analyze coach-specific differences. We investigated three consecutive seasons of an elite team in the German Bundesliga which were managed by three different coaches, respectively. For every season, the formation changes that occurred during games were recorded. The match performance was measured on a team level using the variables “goals,” “chances,” and “scoring zone” entries (≙successful attacking sequence) for the own/opposing team. Non-parametric tests were used to compare the 10 min before with the 10 min after the formation change, as well as games with and without formation change. In the 10 min after the formation change, the team achieved more goals/chances/scoring zone entries than in the 10 min before the formation change (mean ES = 0.52). Similarly, the team conceded fewer opposing goals/chances/scoring zone entries in the 10 min after the formation change (mean ES = 0.35). Furthermore, the results indicate that the success of the respective formation change was dependent on the responsible coach. Depending on the season, the extent of the impacts varied (season 1: mean ES = 0.71; season 2: mean ES = 0.26; and season 3: mean ES = 0.22). Over all three seasons, the formation changes had a positive effect on the match performance of the analyzed team, highlighting their importance in professional soccer. Depending on the season, formation changes had varying impacts on the performance, indicating coach-specific differences. Therefore, the quality of the formation changes of the different coaches varied. The provided information can support coaches in understanding the effects of their in-game decisions.
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